Predictors of Antisocial and Prosocial Behavior in an Adolescent Sports Context

This study examined antisocial and prosocial behavior of N = 439 adolescent athletes between 14 and 17 years of age (67 teams). Multi‐level analyses showed that team membership explained 20 and 13 percent of the variance in antisocial and prosocial behavior in the sports context, respectively. The team effects suggest that aggregating antisocial or prosocial adolescents within teams may partially explain differences in antisocial and prosocial behavior among athletes in the sports context. A trend was found toward a relation between higher levels of moral reasoning within teams, and less antisocial behavior in the sports context. Favorable moral atmosphere was positively associated with more prosocial behavior in the sports context. Finally, supportive coach–athlete relationships were associated with both less antisocial and more prosocial behavior in the sports context.     

Feature significance in generalized additive models

This paper develops inference for the significance of features such as peaks and valleys observed in additive modeling through an extension of the SiZer-type methodology of Chaudhuri and Marron (1999) and Godtliebsen et al. (2002, 2004) to the case where the outcome is discrete. We consider the problem of determining the significance of features such as peaks or valleys in observed covariate effects both for the case of additive modeling where the main predictor of interest is univariate as well as the problem of studying the significance of features such as peaks, inclines, ridges and valleys when the main predictor of interest is geographical location. We work with low rank radial spline smoothers to allow to the handling of sparse designs and large sample sizes. Reducing the problem to a Generalised Linear Mixed Model (GLMM) framework enables derivation of simulation-based critical value approximations and guards against the problem of multiple inferences over a range of predictor values. Such a reduction also allows for easy adjustment for confounders including those which have an unknown or complex effect on the outcome. A simulation study indicates that our method has satisfactory power. Finally, we illustrate our methodology on several data sets.     

GOES-8 X-ray sensor variance stabilization using the multiscale data-driven Haar–Fisz transform

Summary.  We consider the stochastic mechanisms behind the data that were collected by the solar X-ray sensor (XRS) on board the GOES-8 satellite. We discover and justify a non-trivial mean–variance relationship within the XRS data. Transforming such data so that their variance is stable and its distribution is taken closer to the Gaussian distribution is the aim of many techniques (e.g. Anscombe and Box–Cox). Recently, new techniques based on the Haar–Fisz transform have been introduced that use a multiscale method to transform and stabilize data with a known mean–variance relationship. In many practical cases, such as the XRS data, the variance of the data can be assumed to increase with the mean, but other characteristics of the distribution are unknown. We introduce a method, the data-driven Haar–Fisz transform, which uses the Haar–Fisz transform but also estimates the mean–variance relationship. For known noise distributions, the data-driven Haar–Fisz transform is shown to be competitive with the fixed Haar–Fisz methods. We show how our data-driven Haar–Fisz transform method denoises the XRS series where other existing methods fail.     

Bandwidth-based nonparametric inference

Nonparametric curve estimation is an extremely common statistical procedure. While its primary purpose has been exploratory, some advances in inference have been made. This paper provides a critical review of inferential tests that make fundamental use of a key element of nonparametric smoothing, the bandwidth, to determine the significance of certain features. A major focus is on two important problems that have been tackled using bandwidth-based inference: testing for the multimodality of a density and testing for the monotonicity of a regression curve. Early research in bandwidth-based inference is surveyed, as well as recent theoretical advances. Possible future directions in bandwidth-based inference are discussed.     

Bayesian inference for dynamic social network data

We consider a continuous-time model for the evolution of social networks. A social network is here conceived as a (di-) graph on a set of vertices, representing actors, and the changes of interest are creation and disappearance over time of (arcs) edges in the graph. Hence we model a collection of random edge indicators that are not, in general, independent. We explicitly model the interdependencies between edge indicators that arise from interaction between social entities. A Markov chain is defined in terms of an embedded chain with holding times and transition probabilities. Data are observed at fixed points in time and hence we are not able to observe the embedded chain directly. Introducing a prior distribution for the parameters we may implement an MCMC algorithm for exploring the posterior distribution of the parameters by simulating the evolution of the embedded process between observations.     

Retracted and replaced: Sodium cromoglicate: an ineffective drug or meta‐analysis misused?

The following article from Pharmaceutical Statistics, Sodium cromoglicate: an ineffective drug or meta‐analysis misused? by M. T. Stevens, A. M. Edwards, J. B. L. Howell published online on 15 March 2007 in Wiley InterScience ( www.interscience.wiley.com ), has been retracted by agreement between the author, the journal Editor in Chief, Steven Julious, and John Wiley & Sons, Ltd. The retraction has been agreed because the article is not yet ready for publication and an early version without revisions was published in error. Replacement article pending. Copyright © 2007 John Wiley & Sons, Ltd.     

Fratricide identities: the land conflict between indigenous Leco and peasant unions in Apolo,Bolivia

This article explores processes of identity-building and claims-making by rural social groups in the context of recent multicultural and plurinational reforms in Bolivia, focusing on an analysis of the narrative apparatus that underpins a paradigmatic land conflict between an indigenous organization and a peasant union in the Bolivian Amazon. The institutional shift that characterized the country after Evo Morales’ election has been reflected and absorbed at the local level. Here, however, the new claims for recognition cannot be understood only through the –often abused – lenses of ‘resistance struggle’, ‘cultural oppression’ and ‘political discrimination of minorities’. In fact, these claims are the result of a complex interaction between institutional changes, and social actors’ ability to respond to them, proposing powerful narratives that provide society and individuals with new shared meanings and mechanisms of self-identification.     

Making the health care delivery system accountable.

Accountability has become the fact of life for the health care provider and the delivery system. Until recently, accountability has been viewed primarily through the judicial process as issues of fraud and liability, or by managed care entities through evaluation of the financial bottom line. It is this second consideration and its ramifications that will be explored in this article. Appropriate measurement tools are needed to evaluate services, delivery, performance, customer satisfaction, and outcomes assessment. Measurement tools will be considered in light of the industry's unique considerations and realities. All participants, including insurers, employers, management, and health care providers and recipients, bear responsibilities which necessitate assessment and analysis. However, until the basic question, "Who is the customer?" is resolved, accountability issues remain complex and obscured. Accountability costs and impacts must be evaluated over time. They go way beyond bottom line cost containment and reduction. Accountability will be accomplished when the health care industry implements quality and measurement concepts that yield the highest levels of validity and appropriateness for health care delivery.